The education gap over immigration and socioeconomic security

Abstract Worries about polarization are on the rise. In today's Europe, one of the most manifest gaps is the education divide over immigration. Where lower educated citizens tend to be negative about immigration, higher educated individuals are generally positive. Yet the magnitude of this education divide strongly differs between countries. What explains these differences? I theorize that when the levels of socioeconomic security are high, in particular less well educated citizens will be more likely to focus on issues with a strong cultural component, like immigration, and therefore hold more radical opinions. As a result, existing divides will be more pronounced. Analyzing 23 countries between 2002 and 2018, I show that social welfare spending fuels the education divide over immigration. I demonstrate that, indeed, it does so by affecting the immigration attitudes of the less well educated—not those of the better educated.


A: Operationalization of variables
To assess individuals' immigration attitudes, I constructed a scale which consists of three items. Respondents could answer on a 11-points scale what they thought of the following three questions: (1) "Would you say it is generally bad or good for [respondent's country]'s economy that people come to live here from other countries?" (0 = bad; 10 = good); (2) "Would you say that [country]'s cultural life is generally undermined or enriched by people coming to live here from other countries?" (0 = undermined; 10 = enriched); and (3) "Is [country] made a worse or a better place to live by people coming to live here from other countries?" (0 = worse; 10 = better). I have recoded these items so that "0" refers to an anti-immigration attitude and "10" to a pro-immigration attitude (Cronbach's Alpha = 0.85).
To measure education I employed the by the ESS provided ES-ISCED categorization: (I) "less than lower secondary"; (II) "lower secondary"; (IIIb) "upper secondary, vocational, or no access to V1"; (IIIa) "upper secondary, general and/or access to V1"; (IV) "advanced vocational, sub-degree"; (V1) "lower tertiary education, BA level"; (V2) "higher tertiary education, MA level or higher". I dichotomized this variable so that one category (0) represents the lower and middle educated (original categories I through IIIa; 65%) and the other category (1) represents the higher educated (original categories IV through V2; 35%).
To make sure that my assessment of the educational divide over immigration is not confounded by other variables, I controlled for various individual-level characteristics: gender (1 = female), age, whether someone is unemployed (1 = yes) and subjective income. I included a subjective measure of income, because including actual income generates a large number of missing values. The variable is measured by asking respondents how they feel about their household's income. It is measured on a 4-points scale, ranging from "very difficult on present income" (1) to "living comfortably on present income" (4). I also assessed whether someone is a member of a trade union or a similar organization (1 = yes) and whether someone belongs to a minority ethnic group (1 = yes). Finally, I included an item assessing a respondent's religiosity, ranging from "not at all religious" (0) to "very religious" (10).
The main aggregate-level variables are social spending (in percentage of GDP), the harmonized unemployment rate (an internationally comparable measure of the percentage of unemployed people compared to the labor force), GDP per capita, and the Gini coefficient (which measures socioeconomic inequality). The measures of spending, unemployment and GDP all come from the Organisation for Economic Co-operation and Development (OECD). The Gini coefficients are retrieved from Eurostat.
I also included various controls at the aggregate level. First, I included a measure of the strength of liberal democracy. It is measured by means of V-Dem's 'electoral democracy index' (Coppedge et al., 2019). Second, I included trade union density (data come from the OECD). Third, I controlled for religious fractionalization. The data come from a data set compiled by Alesina, Devleeschauwer, Easterly, Kurlat, and Wacziarg (2003). Fourth, I included a variable measuring the percentage of the 25-64 year-old population that has completed tertiary education (retrieved from the OECD). Fifth, because attitudes about immigration might well be affected by the presence of populist radical right parties, I also included a variable measuring the percentage of votes for this party family (votes radical right). The information comes from ParlGov (Döring & Manow, 2021) and the categorization is based on The PopuList (Rooduijn et al., 2019). Finally, I controlled for the percentage of refugees in a country. The data come from the Worldbank.